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Mountain shrublands are widespread habitats of the European Alps. Shrub encroachment into above treeline grazed lands profoundly modifies biodiversity and ecosystem functioning. Yet, mountain shrublands remain overlooked in vegetation distribution modeling because it is difficult to distinguish them from productive grasslands. Here, we used the pigment-sensitive spectral indices based on Sentinel-2 bands within a specific phenological window, to produce a high-resolution distribution map of mountain shrublands in the French Alps. We evaluated the performance of our classification using a large dataset of vegetation plots and found that our model is highly sensitive to Ericaceous species which constitute most of the dense alpine shrublands in the French Alps. Our analysis of topoclimatic and land use factors limiting the shrubland distribution at regional scale found that, consistent with the ecophysiology of shrubs, expansion is limited by a combination of water deficit and temperature. We discussed the past and current land-use implications in the observed distribution and put forward hypotheses combining climate and land-use trajectories. Our work provides a baseline for monitoring mountain shrub dynamics and exploring the response of shrublands to past and ongoing climate and land use changes.

Suitable remote sensing images are crucial for the accurate monitoring of land surface information. Multi-source remote sensing image collaboration is a viable method for obtaining suitable images. Image super-resolution (SR) reconstruction can transcend the mutual limitations of image monitoring range and spatial resolution and provide a solution for multi-source remote sensing image collaboration. However, image SR reconstruction experiences the challenges of low applicability of the modelling dataset, network model, and reconstruction methods. To solve these problems, in this study, two datasets were created: one containing only Gaofen-2 (GF-2) images and the other containing both GF-2 and Sentinel-2 images. Different SR models were established by combining the lightweight-enhanced SR convolutional neural network, enhanced SR generative adversarial network, and dual regression network (DRN). The applicability of the identified SR model was evaluated by applying it to the reconstruction of Sentinel-2 images from different spatiotemporal images. The results indicated that the model formed using the dataset containing both the GF-2 and Sentinel-2 images was highly accurate, with the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) values of 23.2082 dB and 0.6408, respectively. The edges of the plots were refined, and the deformations of the plots, roads, and channels were restored. The SR model formed by the DRN was highly accurate for both single- and multi-source datasets, with PSNR and SSIM values of 24.1548 dB and 0.6912, respectively. Therefore, a method for forming an SR model using a multi-source image dataset combined with the DRN was proposed for multi-source remote sensing collaboration. The method proposed in this study has good applicability to different spatiotemporal images and can provide a reference for subsequent multi-source remote sensing research. [ABSTRACT FROM AUTHOR]

This research used the Sentinel-1 and Sentinel-2 data to map forest canopy cover. The main aim of this study is to assess the capability and value of SAR and Optical image data for mapping, estimating, and monitoring forest canopy cover and its parameters in the Ngel Nyaki Forest Reserve in the Mambilla Plateau, Taraba State, Nigeria. The spatial distribution pattern of forest vegetation cover between 2015 and 2021 was analyzed using a random forest model with Sentinel-1 and maximum likelihood for Sentinel-2 data and the relationship between the biophysical parameter Leaf-Area-Index (LAI) and the spectral indices. The results show changes in canopy cover derived from Sentinel-1 and Sentinel-2 with varying canopy cover frequencies, indicating a decrease from 34% to 27% and 32% to 25% from 2015 to 2021, respectively. The basal area per acre (BA/ac) ranges from 30 to 170 square feet/acre, with a mean of 62.82 square feet/acre and a standard deviation of 37.77 square feet/acre. Moreover, the analysis of biophysical parameters shows that the LAI for the entire forest area was 3.88 in 2015, indicating a relatively dense canopy with substantial leaf cover, while the LAI value of 3.82 in 2021 suggests a slight decrease in leaf area density. [ABSTRACT FROM AUTHOR]

Background: The habitat use of wild ungulates is determined by forage availability, but also the avoidance of predation and human disturbance. They should apply foraging strategies that provide the most energy at the lowest cost. However, due to data limitations at the scale of movement trajectories, it is not clear to what extent even well-studied species such as red deer (Cervus elaphus) trade-off between forage quality and quantity, especially in heterogeneous alpine habitats characterized by short vegetation periods. Methods: We used remote sensing data to derive spatially continuous forage quality and quantity information. To predict relative nitrogen (i.e. forage quality) and biomass (i.e. forage quantity), we related field data to predictor variables derived from Sentinel-2 satellite data. In particular, our approach employed random forest regression algorithms, integrating various remote sensing variables such as reflectance values, vegetation indices and optical traits derived from a radiative transfer model. We combined these forage characteristics with variables representing human activity, and applied integrated step selection functions to estimate sex-specific summer habitat selection of red deer in open habitats within and around the Swiss National Park, an alpine Strict Nature Reserve. Results: The combination of vegetation indices and optical traits greatly improved predictive power in both the biomass (R2 = 0.60, Root mean square error (RMSE) = 88.55 g/m2) and relative nitrogen models (R2 = 0.34, RMSE = 0.28%). Both female and male red deer selected more strongly for biomass (estimate = 0.672 ± 0.059 SE for normalised values for females, and 0.507 ± 0.061 for males) than relative nitrogen (estimate = 0.124 ± 0.062 for females, and 0.161 ± 0.061 for males, respectively). Females showed higher levels of use of the Swiss National Park. Conclusions: Red deer in summer habitats select forage quantity over quality with little difference between sexes. Females respond more strongly to human activities and thus prefer the Swiss National Park. Our results demonstrate the capability of satellite data to estimate forage quality and quantity separately for movement ecology studies, going beyond the exclusive use of conventional vegetation indices. [ABSTRACT FROM AUTHOR]

Total suspended matter is one of the crucial water quality parameters for both inland and marine environments, and a key role in evaluating the water quality of estuaries and offshore areas. Each year, the Yellow River carries a significant amount of sediment into the semi-enclosed Bohai Sea, results in a prolonged high concentration of total suspended matter in the offshore areas of the Yellow River Estuary. This study focuses on the offshore region of the Yellow River Estuary in China. Utilizing Sentinel-2 satellite imagery data from 2020 to 2023 and in-situ measured data from August 2020 to August 2022, to address the lack of physical mechanisms currently studied in machine learning retrieval methods, a model that integrates the physics-driven Quasi-Analytical Algorithm (QAA) and data-driven Random Forest (RF) is employed for the retrieval of total suspended matter concentration in the study area. The fused model (QAA-RF) is compared and analysed against regression models and standalone machine learning models. The results indicate that the accuracy of machine learning retrieval models is consistently higher than that regression models. The QAA-RF model demonstrates the highest accuracy (R2 = 0.87, MAE = 5.01 mg L−1, RMSE = 6.39 mg L−1). Based on the QAA-RF model and the imagery data, monthly total suspended matter concentration is conducted in the study area. The retrieval results indicates that: (1) the total suspended matter concentrations is primarily concentrated in the near estuary region, with concentrations decreasing as distance from the estuary increases. (2) the total suspended matter concentration exhibits a distribution pattern with higher values in spring and winter, and lower values in summer and autumn. (3) the concentration of total suspended matter shows relatively small fluctuations at the annual scale from 2020 to 2023. [ABSTRACT FROM AUTHOR]

Periodic observations of landscape phenology are critical for assessing growth cyclicals of forest covers. The present study attempts to discern the phenology dynamics of dry deciduous forest cover using ground- and satellite-based sensors and to examine their complementarity. Modelled phenophases coming from the data of both sensors reflected dynamics of landscape phenology matching with the pattern of deciduous cover. Phenophases derived from the data of PhenoCam coincided with the observed rainfall dynamics of the region, while satellite-based phenophases could not, largely because of a mismatch in data availability owing to cloud cover hindrance. A correlation was seen between the measured indices of both sensors. The modelled length of the season of deciduous cover obtained from the data of both sensors was similar. The study tries to fill in the existing wide gap in the studies on phenophases of Indian tropical covers and highlights how it can assist better in gauging the ecological dynamics of protected areas. [ABSTRACT FROM AUTHOR]

Satellite-derived bathymetry (SDB) is a vital technique for the rapid and cost-effective measurement of shallow underwater terrain. However, it faces challenges of image noise, including clouds, bubble clouds, and sun glint. Consequently, the acquisition of no missing and accurate bathymetric maps is frequently challenging, particularly in cloudy, rainy, and large-scale regions. In this study, we propose a multi-temporal image weighted composition (MIWC) method. This method performs iterative segmentation and inverse distance weighted composition of multi-temporal images based only on the near-infrared (NIR) band information of multispectral images to obtain high-quality composite images. The method was applied to scenarios using Sentinel-2 imagery for bathymetry of four representative areas located in the South China Sea and the Atlantic Ocean. The results show that the root mean square error (RMSE) of bathymetry from the composite images using the log-transformed linear model (LLM) and the log-transformed ratio model (LRM) in the water depth range of 0–20 m are 0.67–1.22 m and 0.71–1.23 m, respectively. The RMSE of the bathymetry decreases with the number of images involved in the composition and tends to be relatively stable when the number of images reaches approximately 16. In addition, the composition images generated by the MIWC method generally exhibit not only superior visual quality, but also significant advantages in terms of bathymetric accuracy and robustness when compared to the best single images as well as the composition images generated by the median composition method and the maximum outlier removal method. The recommended value of the power parameter for inverse distance weighting in the MIWC method was experimentally determined to be 4, which typically does not require complex adjustments, making the method easy to apply or integrate. The MIWC method offers a reliable approach to improve the quality of remote sensing images, ensuring the completeness and accuracy of shallow water bathymetric maps. [ABSTRACT FROM AUTHOR]

The objective of this work is to update and improve the existing cartography on the spatial distribution of native forests in the Buenos Aires province, Argentina. It was developed within the framework of a project executed by researchers of the national scientific and technological system for the Dirección de Bosques of the Ministerio de Ambiente de la Provincia de Buenos Aires, focusing on the design and execution of a uniform methodology for the identification and delineation of native forest covers for the entire province. The cartographic update used a combination of field-collected information, complemented with visual interpretation of multitemporal series of high-resolution optical images, generalized using machine learning algorithms fed with information derived from Sentinel-2 multispectral satellite image series. The products obtained were evaluated using metrics derived from the contingency matrix, calculated from field-labeled data. The methodology used for automatic classification is detailed, including the methodology for labeling training points, the spectral information used to feed the classifiers, the selection of the classification methodology itself, as well as the details of the post-processing procedure applied to each specific forest formation and the evaluation of the final products obtained. The delineation obtained excludes 235182 ha from the 968397 ha of the current map, which we consider to not correspond to native forests, and incorporates 187512 ha of native forests that had not been previously mapped, reducing the total mapped area of native forests in the province by 4.9%. The evaluation, carried out with 719 field-labeled points, assigns an overall accuracy of 0.89 and a kappa index of 0.85 to the classification obtained, indicating that the proposed methodology is suitable for the delineation of native forests in the province. [ABSTRACT FROM AUTHOR]

Shallow water bathymetry is essential for maritime navigation, environmental monitoring, and coastal management. While traditional methods such as sonar and airborne LiDAR provide high accuracy, their high cost and time-consuming nature limit their application in remote and sensitive areas. Satellite remote sensing offers a cost-effective and rapid alternative for large-scale bathymetric inversion, but it still relies on significant in situ data to establish a mapping relationship between spectral data and water depth. The ICESat-2 satellite, with its photon-counting LiDAR, presents a promising solution for acquiring bathymetric data in shallow coastal regions. This study proposes a rapid bathymetric inversion method based on ICESat-2 and Sentinel-2 data, integrating spectral information, the Forel-Ule Index (FUI) for water color, and spatial location data (normalized X and Y coordinates and polar coordinates). An automated script for extracting bathymetric photons in shallow water regions is provided, aiming to facilitate the use of ICESat-2 data by researchers. Multiple machine learning models were applied to invert bathymetry in the Dongsha Islands, and their performance was compared. The results show that the XG-CID and RF-CID models achieved the highest inversion accuracies, 93% and 94%, respectively, with the XG-CID model performing best in the range from −10 m to 0 m and the RF-CID model excelling in the range from −15 m to −10 m. [ABSTRACT FROM AUTHOR]

Due to the significant threat to forest health posed by beetle infestations on pine trees, timely and accurate predictions are crucial for effective forest management. This study developed a pine tree stress probability prediction workflow based on monthly cloud-free Sentinel-2 composite images to address this challenge. First, representative pine tree stress samples were selected by combining long-term forest disturbance data using the Continuous Change Detection and Classification (CCDC) algorithm with high-resolution remote sensing imagery. Monthly cloud-free Sentinel-2 images were then composited using the Multifactor Weighting (MFW) method. Finally, a Random Forest (RF) algorithm was employed to build the pine tree stress probability model and analyze the importance of spectral, topographic, and meteorological features. The model achieved prediction precisions of 0.876, 0.900, and 0.883, and overall accuracies of 89.5%, 91.6%, and 90.2% for January, February, and March 2023, respectively. The results indicate that spectral features, such as band reflectance and vegetation indices, ranked among the top five in importance (i.e., SWIR2, SWIR1, Red band, NDVI, and NBR). They more effectively reflected changes in canopy pigments and leaf moisture content under stress compared with topographic and meteorological features. Additionally, combining long-term stress disturbance data with high-resolution imagery to select training samples improved their spatial and temporal representativeness, enhancing the model's predictive capability. This approach provides valuable insights for improving forest health monitoring and uncovers opportunities to predict future beetle outbreaks and take preventive measures. [ABSTRACT FROM AUTHOR]

The Ocean Colour Thematic Assembly Centre (OCTAC) of the Copernicus Marine Service delivers state-of-the-art Ocean Colour core products for both global oceans and European seas, derived from multiple satellite missions. Since 2015, the OCTAC has provided global and regional high-level merged products that offer value-added information not directly available from space agencies. This is achieved by integrating observations from various missions, resulting in homogenized, inter-calibrated datasets with broader spatial coverage than single-sensor data streams. OCTAC enhanced continuously the basin-level accuracy of essential ocean variables (EOVs) across the global ocean and European regional seas, including the Atlantic, Arctic, Baltic, Mediterranean, and Black seas. From 2019 onwards, new EOVs have been introduced, focusing on phytoplankton functional groups, community structure, and primary production. This paper provides an overview of the evolution of the OCTAC catalogue from 2015 to date, evaluates the accuracy of global and regional products, and outlines plans for future product development. [ABSTRACT FROM AUTHOR]

Coastal zones, particularly sandy beaches, are highly dynamic environments subject to a variety of natural and anthropogenic forcings. Instantaneous shoreline is a widely used indicator of beach changes in image-based applications, and it can display undulations at different spatial and temporal scales. Megacusps, periodic seaward and landward shoreline perturbations, are an example of such undulations that can significantly modify beach width and impact its usability. Traditionally, the study of these phenomena relied on video monitoring systems, which provide high-frequency imagery but limited spatial coverage. Instead, this study explored the potential of employing multispectral satellite-derived shorelines, specifically from Sentinel-2 (S2) and PlanetScope (PLN) platforms, for characterizing and monitoring megacusps' formation and their dynamics over time. First, a tool was developed and validated to guarantee accurate shoreline detection, based on a combination of spectral indices, along with both thresholding and unsupervised clustering techniques. Validation of this shoreline detection phase was performed on three micro-tidal Mediterranean beaches, comparing with high-resolution orthomosaics and in-situ GNSS data, obtaining a good subpixel accuracy (with a mean absolute deviation of 1.5–5.5 m depending on the satellite type). Second, a tool for megacusp characterization was implemented and subsequent validation with reference data proved that satellite-derived shorelines could be used to robustly and accurately describe megacusps. The methodology could not only capture their amplitude and wavelength (of the order of 10 and 100 m, respectively) but also monitor their weekly–daily evolution using different potential metrics, thanks to combining S2 and PLN imagery. Our findings demonstrate that multispectral satellite imagery provides a viable and scalable solution for monitoring shoreline megacusp undulations, enhancing our understanding and offering an interesting option for coastal management. [ABSTRACT FROM AUTHOR]

Water content is one of the most critical characteristics in plant physiological development. Therefore, this information is a crucial factor in determining the water stress conditions of vegetation, which is essential for assessing the wildfire risk and land management decision-making. Remote sensing can be vital for obtaining information over large, limited access areas with global coverage. This is important since conventional techniques for collecting vegetation water content are expensive, time-consuming, and spatially limited. This work aims to evaluate the vegetation live fuel moisture content (LFMC) seasonal variability using a multiscale remote sensing approach, particularly on rockroses, the Cistus ladanifer species, a Western Mediterranean basin native species with wide spatial distribution, over the Herdade da Mitra at the University of Évora, Portugal. This work used four dataset sources, collected monthly between June 2022 and July 2023: (i) Vegetation samples used to calculate the LFMC; (ii) Vegetation reflectance spectral signature using the portable spectroradiometer FieldSpec HandHeld-2 (HH2); (iii) Multispectral optical imagery obtained from the Multispectral Instrument (MSI) sensor onboard the Sentinel-2 satellite; and (iv) Multispectral optical imagery derived from a camera onboard an Unmanned Aerial Vehicle Phantom 4 Multispectral (P4M). Several temporal analyses were performed based on datasets from different sensors and on their intercomparison. Furthermore, the Random Forest (RF) classifier, a machine learning model, was used to estimate the LFMC considering each sensor approach. MSI sensor presented the best results (R2 = 0.94) due to the presence of bands on the Short-Wave Infrared Imagery region. However, despite having information only in the Visible and Near Infrared spectral regions, the HH2 presents promising results (R2 = 0.86). This suggests that by combining these spectral regions with a RF classifier, it is possible to effectively estimate the LFMC. This work shows how different spatial scales, from remote sensing observations, affect the LFMC estimation through machine learning techniques. [ABSTRACT FROM AUTHOR]

Wastewater treatment (WWT) contributes 2–9% of global greenhouse gas (GHG) emissions. The noticeable uncertainty in emissions estimation is due in large part to the lack of measurement data. Several methods have recently been developed for monitoring fugitive GHG emissions from WWT. However, limited by the short duration of the monitoring, only "snapshot" data can be obtained, necessitating extrapolation of the limited data for estimating annual emissions. Extrapolation introduces substantial errors, as it fails to account for the spatial and temporal variations of fugitive emissions. This research evaluated the feasibility of studying the long-term CH4 emissions from WWT by analyzing high spatial resolution Sentinel-2 data. Satellite images of a WWT plant in Calgary, Canada, taken between 2019 and 2023, were processed to retrieve CH4 column concentration distributions. Digital image processing techniques were developed and used for extracting the time- and space-varying features of CH4 emissions, which revealed daily, monthly, seasonal, and annual variations. Emission hotspots were also identified and corroborated with ground-based measurements. Despite limitations due to atmospheric scattering, cloud cover, and sensor resolution, which affect precise ground-level concentration assessments, the findings reveal the dynamic nature of fugitive GHG emissions from WWT, indicating the need for continuous monitoring. The results also show the potential of utilizing satellite images for cost-effectively evaluating fugitive CH4 emissions. [ABSTRACT FROM AUTHOR]

Marine and freshwater plastic pollution is a worldwide problem affecting ecosystems and human health. Although remote sensing has been used to map large floating plastic rafts, there are research gaps in detecting submerged plastic due to the limited amount of in situ data. This study is the first to collect in situ data on submerged and floating plastics in a freshwater environment and analyse the effect of water submersion on the strength of the plastic signal. A large 10 × 10 m artificial polymer tarpaulin was deployed in a freshwater lake for a two-week period and was captured by a multi-sensor and multi-resolution unmanned aerial vehicle (UAV) and satellite. Spectral analysis was conducted to assess the attenuation of individual wavelengths of the submerged tarpaulin in UAV hyperspectral and Sentinel-2 multispectral data. A K-Means unsupervised clustering algorithm was used to classify the images into two clusters: plastic and water. Additionally, we estimated the optimal number of clusters present in the hyperspectral dataset and found that classifying the image into four classes (water, submerged plastic, near surface plastic and buoys) significantly improved the accuracy of the K-Means predictions. The submerged plastic tarpaulin was detectable to ~0.5 m below the water surface in near infrared (NIR) (~810 nm) and red edge (~730 nm) wavelengths. However, the red spectrum (~669 nm) performed the best with ~84% true plastic positives, classifying plastic pixels correctly even to ~1 m depth. These individual bands outperformed the dedicated Plastic Index (PI) derived from the UAV dataset. Additionally, this study showed that in neither Sentinel-2 bands, nor the derived indices (PI or Floating Debris Index (FDI), it is currently possible to determine if and how much of the tarpaulin was under the water surface, using a plastic tarpaulin object of 10 × 10 m. Overall, this paper showed that spatial resolution was more important than spectral resolution in detecting submerged tarpaulin. These findings directly contributed to Sustainable Development Goal 14.1 on mapping large marine plastic patches of 10 × 10 m and could be used to better define systems for monitoring submerged and floating plastic pollution. [ABSTRACT FROM AUTHOR]

This study investigates the uncertainty of satellite-derived bathymetry (SDB) in turbid port environments by integrating multi-temporal composites of Sentinel-2 and Landsat 8 satellite imagery with in situ bathymetry and turbidity data. The research aims to evaluate the effectiveness of SDB and its spatiotemporal correlation with satellite-based turbidity indicators across seven Chinese port areas. Results indicate that both Sentinel-2 and Landsat 8, using a three-band combination, achieved comparable performance in SDB estimation, with R2 values exceeding 0.85. However, turbidity showed a negative correlation with SDB accuracy, and higher turbidity levels limited the maximum retrievable water depth, resulting in SDB variances ranging from 0 to 15 m. Landsat 8 was more accurate in low to moderate turbidity environments (12–15), where SDB variance was lower, while higher turbidity (above 15) led to greater SDB variance and reduced accuracy. Sentinel-2 outperformed Landsat 8 in moderate to high turbidity environments (36–203), delivering higher R2 values and more consistent SDB estimates, making it a more reliable tool for areas with variable turbidity. These findings suggest that SDB is a viable method for bathymetric and turbidity mapping in diverse port settings, with the potential for broader application in coastal monitoring and marine management. [ABSTRACT FROM AUTHOR]

Optical remote sensing images have a wide range of applications but are often affected by cloud cover, which interferes with subsequent analysis. Therefore, cloud removal has become indispensable in remote sensing data processing. The Tibetan Plateau, as a sensitive region to climate change, plays a crucial role in the East Asian water cycle and regional climate due to its snow cover. However, the rich ice and snow resources, rapid snow condition changes, and active atmospheric convection in the plateau as well as its surrounding mountainous areas, make optical remote sensing prone to cloud interference. This is particularly significant when monitoring snow cover changes, where cloud removal becomes essential considering the complex terrain and unique snow characteristics of the Tibetan Plateau. This paper proposes a novel Multi-Scale Attention-based Cloud Removal Model (MATT). The model integrates global and local information by incorporating multi-scale attention mechanisms and local interaction modules, enhancing the contextual semantic relationships and improving the robustness of feature representation. To improve the segmentation accuracy of cloud- and snow-covered regions, a cloud mask is introduced in the local-attention module, combined with the local interaction module to modulate and reconstruct fine-grained details. This enables the simultaneous representation of both fine-grained and coarse-grained features at the same level. With the help of multi-scale fusion modules and selective attention modules, MATT demonstrates excellent performance on both the Sen2_MTC_New and XZ_Sen2_Dataset datasets. Particularly on the XZ_Sen2_Dataset, it achieves outstanding results: PSNR = 29.095, SSIM = 0.897, FID = 125.328, and LPIPS = 0.356. The model shows strong cloud removal capabilities in cloud- and snow-covered areas in mountainous regions while effectively preserving snow information, and providing significant support for snow cover change studies. [ABSTRACT FROM AUTHOR]

Landslides represent a growing global geological hazard, further intensified by climate‐induced changes. Remote sensing data, through its capacity for repetitive collection and change detection techniques, that compare and quantify the spatio‐temporal alterations over time, plays a critical role in landslide detection. Considering the February 2023 São Sebastião event and Sentinel‐2 imagery, we assessed diverse unsupervised change detection techniques, encompassing both traditional and recent machine learning‐based approaches. Notably, the Floating References (FR) and Homogeneous Blocks Single‐class Classification (HBSC) methods outperform classic approaches and deliver the most accurate results with F1‐Score and kappa coefficient exceeding 0.96 and 0.92, respectively. These outcomes demonstrate the efficacy of machine learning in automating landslide delineation and underscore the necessity of meticulous data and parameter selection in achieving high‐accuracy automatic landslide mapping. Lastly, this study fills a significant gap in the existing literature by evaluating unsupervised change detection methods for landslide mapping within the Brazilian context. [ABSTRACT FROM AUTHOR]

Within precision agriculture, yield mapping is important in the evaluation of crop management and delineation of management zones. It can also be used to assess within-field yield potential, in order to guide different precision agriculture practices. However, some farmers do not have a yield monitoring system, and some who do may obtain incomplete or erroneous yield data. This study examined the accuracy with which winter wheat (Triticum aestivum L.) yield could be mapped in 18 fields in southern Sweden using a simple empirical relationship between Sentinel-2 (ESA, Paris, France) data, vegetation index (VI) maps and combined harvester data collected in nearby fields. The results showed that a decrease in map resolution to 40 m reduced the error in the yield maps obtained. Normalised difference water index (NDWI) was the most efficient VI, while a combination of satellite data from earlier and later plant development (booting and milk development stages) performed slightly better than data for other development stages and combinations. The best-performing model at a within-field scale (40-m resolution) had an average mean absolute error (MAE) of 0.40 tonnes ha−1 in a leave-one-field-out cross validation. When the prediction model at field-means scale was applied on 69 farms in a 1055 km2 area, MAE was 0.75 tonnes ha−1 when comparing predictions with mean yields reported by farmers in a phone survey. Therefore, if adequate combined harvester and/or mean yield data are available, a modelling framework that translates satellite imagery into yield maps on-the-fly could be made available for different stakeholders via decision support systems for precision agriculture. [ABSTRACT FROM AUTHOR]

As an essential component of the intertidal zone, tidal flats (TFs) are areas rich in resources where with the most intense material and energy exchanges. However, due to the dual threats of human activities and extreme climate conditions, TFs are disappearing on a large scale. Despite their importance, accurately mapping TFs has proved challenging due to their complex and dynamic nature. Nevertheless, Tidal influences significantly enhance the diversity and variability of TFs, and suspended particulates introduce turbidity that challenges conventional indices used for distinguishing between water and land. This study focuses on the world's largest intertidal sedimentary system located along the central coast of Jiangsu, an area characterized by complex sedimentary features and dynamic TF conditions. Through quantitative analysis of the spectral characteristics of TFs at different years, seasons, and tidal stages, this study identifies two unique spectral features of TFs: uniformly low reflectance values and a trapezoidal spectral shape. Leveraging the low reflectance, the flatness of the middle segment in the trapezoidal spectral shape, and the initial increase followed by a decreasing trend across critical bands, a novel Tidal Flat Index (TFI) has been developed. Experimental results indicate that TFI is suitable for robust and direct TF mapping across years, seasons, and tidal stages, achieving F1 scores exceeding 0.95 in 12 different scenarios. Compared to other indices and rule-based methods, TFI offers greater accuracy, threshold stability, background and cloud suppression. The study also extends to other globally rich TFs regions to demonstrate the universality and applicability of the proposed index in various environments, including its effectiveness in delineating annual TFs extents. This study offers technical support for the automatic mapping of TFs based on single Sentinel-2 multispectral images. [ABSTRACT FROM AUTHOR]

• An automatic burned area mapping algorithm based on active fire data and Sentinel-2 Level 2A imagery is presented. • Good concordance of the proposed algorithm with reference sources. • Global commission errors are higher than omission errors. • Forest fires in tropical and temperate forest are the least accurately mapped ecosystems. • Greater accuracy of the proposed algorithm in comparison to FIRECCI51 and MCD64A1. Understanding the spatial and temporal trends of burned areas (BA) on a global scale offers a comprehensive view of the underlying mechanisms driving fire incidence and its influence on ecosystems and vegetation recovery patterns over extended periods. Such insights are invaluable for modeling fire emissions and the formulation of strategies for post-fire rehabilitation planning. Previous research has provided strong evidence that current global BA products derived from coarse spatial resolution data underestimates global burned areas. Consequently, there is a pressing need for global high-resolution BA products. Here, we present an automatic global burned area mapping algorithm (Sentinel2BAM) based on Sentinel-2 Level-2A imagery combined with Visible Infrared Imaging Radiometer Suite (VIIRS) and Moderate Resolution Imaging Spectrometer (MODIS) active fire data. The algorithm employs a Random Forest Model trained by active fires to predict BA probabilities in each 5-day Normalized Burn Ratio (NBR) index-based temporal composites. In a second step, a time-series and object-based analysis of the estimated BA probabilities allows burned areas to be detected on a quarterly basis. The algorithm was implemented in Google Earth Engine (GEE) and applied to 576 Sentinel-2 tiles corresponding to 2019, distributed globally, to assess its ability to map burned areas across different ecosystems. Two validation sources were employed: 21 EMSR Copernicus Emergency Service perimeters obtained using high spatial resolution (<10 m) data (EMSR21) located in the Mediterranean basin and 50 20x20 km global samples selected by stratified sampling with Sentinel-2 at 10 m spatial resolution (GlobalS50). Additionally, 105 Landsat-based long sample units (GlobalL105), were employed to compare the performance of the Sentinel2BAM algorithm against the FIRECCI51 and MCD64A1 global products. Overall accuracy metrics for the Sentinel2BAM algorithm, derived from validation sources highlight higher commission (CE) than omission (OE) errors (CE=10.3 % and OE=7.6 % when using EMSR21 as reference, CE=18.9 % and OE=9.5 % when using Global S50 as reference), while GlobalL105-based inferenced global comparison metrics show similar patterns (CE=22.5 % and OE=13.4 %). Results indicate differences across ecosystems: forest fires in tropical and temperate biomes exhibit higher CE, mainly due to confusion between burned areas and croplands. According to GlobalL105, Sentinel2BAM shows greater accuracy globally (CE=22.5 %, OE=13.4 %) compared to FIRECCI51 (CE=20.8 %, OE=46.5 %) and MCD64A1 (CE=17.5 %, OE=53.1 %), substantially improving the detection of small fires and thereby reducing omission errors. The strengths and weaknesses of the algorithm are thoroughly addressed, demonstrating its potential for global application. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is the main component of soil organic matter (SOM) and constitutes the crucial component of the soil. It supports key soil functions, stabilizes soil structure, aid in plant-nutrient retention and release, and promote water infiltration and storage. Predicting SOC using Sentinel-2 data integrated with machine learning algorithms under zero tillage practice is inadequately documented for developing countries like Zimbabwe. The purpose of this study is to evaluate the performance of support vector machine (SVM), artificial neural network (ANN), and partial least square regression (PLSR) algorithms from Sentinel-2 data for SOC estimation. The SVM, ANN and PLSR models were used with a cross-validation to estimate the SOC content based on 50 georeferenced calibration samples under a zero-tillage practice. The ANN model outperformed the other two models by delivering a coefficient of determination (R2) of between 55 and 60% of SOC variability and RMSE varied between 5.01 and 8.78%, whereas for the SVM, R2 varied between 0.53 and 0.57 and RMSE varied between 6.25 and 11.39%. The least estimates of SOC provided by the PLSR algorithm were, R2 = 0.44–0.49 and RMSE = 7.59–12.42% for the top 15 cm depth. Results with and R2, root mean square error (RMSE) and mean absolute error (MAE) for SVM, ANN and PLSR, show that the ANN model is highly capable for capturing SOC variability. Although the ANN algorithm provides more accurate SOC estimates than the SVM algorithm, the difference in accuracy is not significant. Results revealed a satisfactory agreement between the SOC content and zero tillage practice (R2, coefficient of variation (CV), MAE, and RMSE using SVM, ANN and PLSR for the validation dataset using four predictor variables. The calibration results of SOC indicated that the mean SOC was 15.83% and the validation mean SOC was 17.02%. The SOC validation dataset (34.17%) had higher degree of variation around its mean as compared to the calibration dataset (29.86%). The SOC prediction results can be used as an important tool for informed decisions about soil health and productivity by the farmers, land managers and policy makers. [ABSTRACT FROM AUTHOR]

Multitemporal imagery offers a critical advantage by capturing seasonal variations, which are essential for differentiating between land use and land cover (LULC) types. While these types may appear similar when examined at one specific time, they exhibit distinct phenological patterns across different seasons. This temporal depth is crucial for enhancing model accuracy, particularly in heterogeneous landscapes where LULC transitions are frequent and complex. This paper made use of spectral bands and indices of Sentinel-2 from April to September 2020 for LULC classification using two advanced machine learning models: Random forest (RF) and support vector machine (SVM). The spectral indices include the normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and modified normalized water index (MNDWI). The dataset was divided into four temporal feature sets: April-May, June-July, August-September, and the entire period from April-September. For each two-month period, the median values of the spectral bands and indices were used. Both models were evaluated based on overall accuracy, F1-score, Kappa coefficient, precision, and recall. Results indicate that incorporating multitemporal features enhanced the performance of the chosen models, with overall accuracy increasing from 82.4% to 94.03% for RF and from 75.4% to 93.54% for SVM. Additionally, the RF algorithm demonstrated higher accuracy than the SVM model across various time periods, with notable improvements in other performance metrics. These improvements also underscore the ability of the models to leverage the rich multitemporal data provided by Sentinel-2 for accurate LULC classification. This study highlights the importance of incorporating the dynamics of features in remote sensing applications to enhance the precision and reliability of LULC classification. [ABSTRACT FROM AUTHOR]

By monitoring evapotranspiration (ET), the exchange of water and energy between the soil, plants, and the atmosphere can be controlled. Routine estimations of ET on a daily, monthly, and seasonal basis can give relevant information on small-scale agricultural practices, such as the Ribb watershed in Ethiopia. However, MODIS sensors have recently given high temporal resolution ET products across large areas, but their low spatial resolution limits its application on a local scale. The primary goal of the study was to downscale the MODIS ET (1 km) product to a finer spatial resolution at the watershed level. The model's 12 predictor variables (NDVI, EVI, LAI, FVC, SAVI, NDMI, NDWI, Albedo, emissivity, LST, and DEM: slope and elevation) were produced using the random forest (RF) algorithm using Sentinel-2 (S-2) 20 m and Landsat-8 (L-8) 30 m. The RF regression model was used to assess the relationship between predicted variables and downscaled MODIS ET. The FAO-PM ET model, developed from meteorological stations, was validated by R 2 and RMSE for three seasons (rainy, post-rainy, and dry) in 2022. The results were in good agreement with MODIS ET, with an RMSE of 0.22 for S-2 and 0.28 for L-8. In the FAO-PM ET model, the downscaled result showed greater spatial details and better agreement with gage station readings ( R 2 ≈ 0.88 and 0.82 ). Thus, considering the effectiveness and simplicity of machine learning techniques, our study demonstrated the potential for ET downscaling. Furthermore, the study suggests integrating spatiotemporal time series data to reach higher resolution. [ABSTRACT FROM AUTHOR]

The main purpose of the work was to verify the applicability of the approach to assessing the ecological state of soils and a theoretical model of the response of a living being in the form of a vegetation index to the impact of a complex of heavy metals in the zone of impact of coal-mining and -processing facilities in the conditions of the Far Eastern taiga-forest soil-bioclimatic region. The research was carried out in conjunction with a geochemical assessment of natural environments, which made it possible to consider the size of the impact zones of coal-mining and -processing facilities on the territory of the emission quota experiment conducted by the Ministry of Natural Resources of the Russian Federation. The inventory of emission sources allowed us to identify several priority areas: the main industrial site, an industrial boiler house, an integrated industrial site, two coal mines, etc. According to the results of the summary calculations of the dispersion of pollutants, the following priority pollutants were identified: carbon (soot) and inorganic and coal dust. The determination of soil and snow pollution levels made it possible to identify the localization of technogenic anomalies of heavy metals, as well as to determine the dust load on the studied area. The soil cover is characterized by a low level of pollution in terms of the total pollution index, however, the accumulation of trace elements in the surface layer of soils is typical for most of the studied metals and metalloids, as evidenced by concentration coefficients >1. Centers of snow cover pollution have been identified, which are confined to the residential area. Areas with very high levels of snow pollution are located near municipal boiler houses. Based on a theoretical model of the response of a living being in the form of a vegetation index to the effect of a complex of heavy metals of a certain set contained in the soils of the vicinity of a coal-mining and -processing enterprise, maximum permissible levels of their content have been established. The boundaries of the zone of influence of the main structural divisions of the coal-mining enterprise, beyond which the territories of the natural and anthropogenic background are distributed, are determined. The soils in the vicinity of coal-mining and -processing facilities are characterized mainly by a background (undisturbed) condition or are poorly degraded, and, thus, no harm has been done to the soils. [ABSTRACT FROM AUTHOR]

The global decline in tall vegetation (TV) due to environmental degradation necessitates effective detection methods for conservation and sustainable development goals. This study presents a novel machine learning (ML) approach for mapping TV cover changes at a pixel level (10 m) using information fusion of Synthetic Aperture Radar (SAR) and multispectral optical data. Demonstrated across the Roorkee-Dehradun Bypass in India, the approach integrates Sentinel-1 SAR and Sentinel-2 multispectral data, validated with field observations. Our method, which utilizes a total of fourteen features extracted from both data sources, was rigorously tested with six ML models. Random Forest notably outperformed other models, achieving the highest classification accuracy of 96.5% when fusing SAR and optical data. This performance is closely followed by the XGBoost and LightGBM models. The integration of both data types leverages complementary features, significantly enhancing model performance compared to using each data type individually. The analysis revealed a consistent decrease in TV from 2016 to 2018, aligning with observed changes in the study regions. By effectively identifying and classifying TV changes due to highway construction and urbanization, our approach provides a valuable tool for monitoring environmental degradation. This work highlights the potential of integrating SAR and optical data through information fusion to achieve more accurate and reliable change detection, offering a significant advancement in fine-resolution environmental monitoring and conservation management. [ABSTRACT FROM AUTHOR]

This study investigates the intricate dynamics of a 60-kilometer section of India's eastern coast, from the Raslupur River mouth to Udaypur Beach in West Bengal. This research thoroughly analyzes littoral changes using synthetic aperture radar (SAR) and optical remote sensing data. Herein, Multi-temporal Landsat series satellite data from 1975 to 2009, Sentinel-1 SAR data from 2019 and Sentinel-2 data from 2015 are used. The investigation yielded extraordinary insights into coastal change metrics, including an annual average rate of change of 1.08 m. Shoreline adjustments were quantified using metrics such as End Point Rate (EPR), Linear Regression (LR), Net Shoreline Movement (NSM), and Shoreline Change Envelope (SCE). Over the study area, the annual erosion rate varies from 0.62 m to 2.60 m, and Accretion rates range from 0.02 to 5.99 m. Using the Linear Regression Rate (LRR) model, this investigation predicted landward retreat and seaward advancement of the littoral. Shankarpur and Chandapur, in particular, were anticipated to experience landward migration of 36 m (2019 to 2029) and 67.49 m (2019 to 2039), respectively. Phichhaboni, Haripur, Junput, and Bankiput sea coastlines were predicted to experience 67 to 157 m of seaward accretion. Notably, the research highlights the various factors that influence coastal dynamics, such as sea-level rise, cyclones, sediment regime, deforestation, urbanization, and anthropogenic changes such as groundwater exploitation and artificial barriers. This study provides stakeholders, policymakers, and researchers interested in coastal regions' preservation and sustainable development with actionable insights. This manuscript is an exhaustive guide fostering informed decision-making and effective coastal stewardship. [ABSTRACT FROM AUTHOR]

Data from the new hyperspectral satellite missions such as EnMAP are anticipated to refine leaf area index (LAI) or canopy closure (CC) monitoring in conifer-dominated forest areas. We compared contemporaneous multispectral and hyperspectral satellite images from Sentinel-2 MSI (S2) and EnMAP and assessed whether hyperspectral images offer added value in estimating LAI, effective LAI (LAIeff), and CC in a European boreal forest area. The estimations were performed using univariate and multivariate generalized additive models. The models utilized field measurements of LAI and CC from 38 forest plots and an extensive set of vegetation indices (VIs) derived from the satellite data. The best univariate models for each of the three response variables had small differences between the two sensors, but in general, EnMAP had more well-performing VIs which was reflected in the better multivariate model performances. The best performing multivariate models with the EnMAP data had ~1–6% lower relative RMSEs than S2. Wavelengths near the green, red-edge, and shortwave infrared regions were frequently utilized in estimating LAI, LAIeff, and CC with EnMAP data. Because EnMAP could estimate LAI better, the results suggest that EnMAP may be more useful than multispectral satellite sensors, such as S2, in monitoring biophysical variables of coniferous-dominated forests. [ABSTRACT FROM AUTHOR]

Species-specific timber volume estimates are required to support forest planning and conservation. We evaluated whether additional predictors from a Sentinel-2 time series can improve airborne laser scanning (ALS)-based estimation of species-specific timber volumes. Furthermore, we determined the satellite image dates that provided the greatest improvement in accuracy. The Sentinel-2 time series was constructed to cover 1 March–30 November time-period, with a focus on late spring and early summer. The estimation was done using the k Most Similar Neighbor method and predictors extracted from the ALS data and Sentinel-2 images. Our best model included both ALS and Sentinel-2 time-series predictors, and the relative root mean square error (RMSE) values for pine, spruce and deciduous timber volumes were 40.8%, 57.0% and 51.3%, respectively (mean 49.7%). All deciduous trees were treated as one species. When bands from an individual image were used instead of the time series, the best result was obtained with an image from September where the respective relative RMSE values were 42.2% (deciduous), 58.4% (pine) and 60.6% (spruce) with a mean value of 53.7%. A fusion of a Sentinel-2 time-series and ALS data can improve species-specific estimation results compared to the use of individual Sentinel-2 images or ALS only. [ABSTRACT FROM AUTHOR]

Quantifying the carbon-stocking contribution of forest plantations is a crucial but challenging and expensive process, usually performed through field analysis. For this reason, plantations' carbon storage is often calculated and reported using generic and inaccurate functions relying exclusively on tree species and plantation age. This study introduces a new field-independent (FI) method for forest plantations' carbon quantification and mapping through automatic analysis of Sentinel-2 data. The study area is a Guatemalan forest plantation of 20 hectares, for which we constructed a reference dataset measuring in the field the diameter and the height of all trees within 20 randomly selected plots (10-meter radius). The CO2 equivalent absorbed by the plantation was first estimated using ground data and a design-based (DB) approach. Then, to obtain CO2 equivalent estimates but also maps, we used both ground and Sentinel-2 data to compare a standard model-assisted (MA) approach relying on Random Forests with the FI approach. Our results demonstrate that the FI method provides carbon stock statistics comparable to those obtained using DB and MA methods and more accurate maps. Accordingly, the RMSE obtained using the FI method was 34% while that obtained by the MA method – exploiting random forest algorithm – was greater (RMSE = 39%). The 95% confidence interval estimates of the CO2 stored in the plantation were 100 ± 18 MgC ha−1 and 102 ± 8 MgC ha−1, for DB and MA respectively. Using the FI method, the CO2 ranged between 89 and 117 Mg C ha−1, all values within the DB confidence interval. In addition, the FI map was surprisingly consistent with the MA-derived map, making our approach a valid alternative for monitoring plantation status and carbon storage when ground data are not available. [ABSTRACT FROM AUTHOR]

This study analyzes sea surface water turbidity variability in the coastal region of the Yellow Sea and investigates the relationships with environmental factors. By applying spectral analysis, harmonic analysis, wavelet transformation, and multivariate linear regression to in situ measurements of seawater turbidity data, we examined the temporal turbidity variability and the fractional contributions of tidal currents and atmospheric components to turbidity variations. A multivariate linear regression was used to determine the daily variations in the fractional contributions of tidal currents and atmospheric components to turbidity. The results showed that seawater turbidity exhibited dominant fluctuations corresponding to major tidal harmonics (M2, S2, K1, and O1), along with shallow-water tides (MS4 and M4) and the M2 variation-related semi-diurnal tides (H1 and H2). The amplitudes of turbidity variations associated with these shallow-water tides and semi-diurnal tides demonstrated a linear relationship with those of the tides, with a rate of 0.24 NTU cm−1. This rate was four times greater than the rate of 0.06 NTU cm⁻1 observed for the major tides, addressing the important roles of non-linear tidal processes in shallow regions. In addition, an inertial motion in coastal turbidity (19.56 h) was detected when external forces were relatively weak. The impact of tidal currents on turbidity fluctuations was significantly reduced during typhoon periods due to pronounced atmospheric influences. Investigations of high-resolution Sentinel-2 turbidity over the past 6 years (2018–2023) illustrated more distinctive responses to extreme environmental conditions. This study is expected to enhance the comprehensive understanding of turbidity fluctuations and their linkages to tidal currents and atmospheric conditions in the Yellow Sea. [ABSTRACT FROM AUTHOR]

The study examined the core, buffer, and transition zones within the Cat Ba Biosphere Reserve in Vietnam to assess changes in five land cover categories (water surface, cropland, vacant land, residential land, and forestland) between 2018 and 2022. Additionally, we aimed to evaluate water quality using three indicators (normalized difference turbidity index, normalized difference chlorophyll index, total suspended solids) and air quality using four indicators (nitrogen dioxide, sulfur dioxide, carbon monoxide, ozone) for the 2022 data. Land cover change analysis and monitoring of water and air quality indicators were conducted using the Google Earth Engine platform, employing Sentinel-2 imagery for land cover and water quality assessment and Sentinel-5P images for air quality assessment. The findings revealed correlations between changes in land cover and water and air quality indicators within the Cat Ba Biosphere Reserve. While occasional instances of values surpassing permissible limits were noted, the average values over the 12-month period in 2022 indicated that water and air quality indicators remained within acceptable thresholds. These results suggest effective preservation efforts in the Cat Ba Biosphere Reserve, indicating no significant signs of degradation. [ABSTRACT FROM AUTHOR]

Introduction: Suspended particulate matter (SPM) is a critical indicator of water quality and has a significant impact on the nearshore ecological environment. Consequently, the quantitative evaluation of SPM concentrations is essential for managing nearshore environments and planning marine resources. Methods: This study utilized Sentinel-2's single band and water index variables to develop a remote sensing inversion model for oceanic SPM in the estuary of the Pinglu Canal in China. Six machine learning algorithms were employed: K-nearest neighbor regression (KNNR), AdaBoost regression (ABR), random forest (RF), gradient boosting regression (GBR), extreme gradient boosting regression (XGBR), and light generalized boosted regression (LGBM). The model with the optimal performance was then selected for further analysis. This research applied the established model to investigate the spatial-temporal dynamics of SPM from 2021 to 2023. Results: The findings indicated that (1) the XGBR algorithm exhibited superior performance (R2 = 0.9042, RMSE = 3.0258 mg/L), with LGBM (R2 =0.8258, RMSE = 4.0813 mg/L) and GBR (R2 = 0.823, RMSE = 4.3477 mg/L) also demonstrating effective fitting. However, the ABR, RF, and KNNR algorithms produced less satisfactory fitting results. (2) Additionally, the study revealed that the combination of input variables in the XGBR algorithm was more accurate than single-variable inputs. (3) The contribution of single-band variables to the XGBR algorithm surpassed that of water index variables, with B12, B4, and B11 emerging as the top three influential variables in the model. (4) The annual SPM concentration in the study area exhibited an overall increasing trend, while its spatial distribution generally decreased from the estuary toward the Maowei Sea and Qinzhou Bay. Discussion: The combination of Sentinel-2 data and XGBR model has shown good performance in retrieving SPM concentration, providing a new method and approach for large-scale estimation of SPM concentration. [ABSTRACT FROM AUTHOR]

Climate change has led to an increase in fire frequency in some areas on Earth. Satellite-derived spectral indices such as the Burn Area Index (BAI), the Normalized Burn Ratio (NBR), and their various derivatives are important for monitoring fires. The Burn Area Index for Sentinel-2 (BAIS2), which relies on the spectral bands of Sentinel-2, has a limitation due to its dependence on the red-edge spectral region unique to this satellite. To address this limitation, two new spectral indices (the Optimized Burned Area Index (OBAI) and the Optimized Burned Area Thermal Index (OBATI)) for burned scar mapping have been proposed based on the spectral separability concept and inter-band correlations. These indices aimed to improve the effectiveness of burned scar mapping by utilizing a wider range of spectral bands, including Landsat-8 thermal band and visible, near infrared (NIR), and mid infrared (MIR) regions of either Landsat-8 or Sentinel-2 sensors, which are more responsive to burned scars. We used spatial and spectral accuracy assessments to compare the effectiveness of the standard burned scar mapping indices (BASI2 and NBR) with the newly developed indices in discriminating the burned scars in several world regions. Based on Emergency Management Service fire perimeters, the vector distance algorithm (VDA) revealed that the accuracy of the fire perimeters extracted from the newly developed index, OBAI, was more reliable than the perimeters extracted using the BAIS2 and NBR. The BAIS2 index had the lowest performance in terms of spectral sensitivity compared to the other indices in the detection of burned scars. The performance of the developed optical index was high once the Landsat-8 data used to calculate it compared to Sentinel-2 data. Our findings could be used to further optimize global burned scar products derived from spaceborne data. [ABSTRACT FROM AUTHOR]

Taking Maiji District in Gansu Province as the research area, object-oriented method was used to identify abandoned land based on Sentinel-2 remote sensing satellite data, the overall classification accuracy reached 92%, and the Kappa coefficient was 0.82. The spatial statistical results showed that the abandoned land area in Maiji District was 12 600.31 hm², accounting for 3.62% of the total area of Maiji District and 22.02% of the total cultivated land area. Slope analysis and traffic condition analysis found that terrain factors and traffic conditions were important reasons for abandonment. A spatial autocorrelation analysis of abandoned land in Maiji District revealed significant spatial clustering characteristics. [ABSTRACT FROM AUTHOR]

Tidal flat ecosystems, are under steady decline due to anthropogenic pressures including sea level rise and climate change. Monitoring and managing these coastal systems requires accurate and up-to-date mapping. Sediment characteristics and macrozoobenthos are major indicators of the environmental status of tidal flats. Field monitoring of these indicators is often restricted by low accessibility and high costs. Despite limitations in spectral contrast, integrating remote sensing with deep learning proved efficient for deriving macrozoobenthos and sediment properties. In this study, we combined deep-learning features derived from Sentinel-2 images and Object-Based Image Analysis (OBIA) to explicitly include spatial aspects in the prediction of tsediment and macrozoobenthos properties of tidal flats , as well as the distribution of four benthic species. The deep-learning features extracted from a convolutional autoencoder model were analysed with OBIA to include spatial, textural, and contextual information. Object sets of varying sizes and shapes based on the spectral bands and/or the deep-learning features, served as the spatial units. These object sets and the field-collected points were used to train the Random Forest prediction model. Predictions were made for the tidal basins Pinkegat and Zoutkamperlaag in the Dutch Wadden Sea for 2018 to 2020. The overall prediction scores of the environmental variables ranged between 0.31 and 0.54. The species-distribution prediction model achieved accuracies ranging from 0.54 to 0.68 for the four benthic species). There was an average improvement of 21% points on predictions using objects with deep learning features compared to the pixel-based predictions with just the spectral bands. The mean spatial unit that captured the patterns best ranged between 0.3 ha and 13 ha for the different variables. Overall, using both OBIA and deep-learning features consistently improved the predictions, making it a valuable combination for monitoring these important environmental variables of coastal regions. [ABSTRACT FROM AUTHOR]

In the process of radiometric calibration, the corrections for bidirectional reflectance distribution functions (BRDFs) and spectral band adjustment factors (SBAFs) are crucial. Time-series MODIS images are commonly used to construct BRDFs by using the Ross–Li model in current research. However, the Ross–Li BRDF model is based on the linear relationship between the kernel models and is unable to take into account the nonlinear relationship between them. Furthermore, when using SBAF to account for spectral difference, a radiative transfer model is often used, but it requires many parameters to be set, which may introduce more errors and reduce the calibration accuracy. To address these issues, the random forest algorithm and a spectral interpolation convolution method using the Sentinel-2/multispectral instrument (MSI) are proposed in this study, in which the HuanJing-2A (HJ-2A)/charge-coupled device (CCD3) sensor is taken as an example, and the Dunhuang radiometric calibration site (DRCS) is used as a radiometric delivery platform. Firstly, a BRDF model by using the random forest algorithm of the DRCS is constructed using time-series MODIS images, which corrects the viewing geometry difference. Secondly, the BRDF correction coefficients, MSI reflectance, and relative spectral responses (RSRs) of CCD3 are used to correct the spectral differences. Finally, with the validation results, the maximum relative error between the calibration results of the proposed method and the official calibration coefficients (OCCs) published by the China Centre for Resources Satellite Data and Application (CRESDA) is 3.38%. When tested using the Baotou sandy site, the proposed method is better than the OCCs of the average relative errors calculated for all the bands except for the near-infrared (NIR) band, which has a larger error. Additionally, the effects of the light-matching method and the radiative transfer method, different approaches to constructing the BRDF model, using SBAF to account for spectral differences, different BRDF sources, as well as the imprecise viewing geometrical parameters, spectral interpolation method, and geometric positioning error, on the calibration results are analyzed. Results indicate that the cross-calibration coefficients obtained using the random forest algorithm and the proposed spectral interpolation method are more applicable to the CCD3; thus, they also account for the nonlinear relationships between the kernel models and reduce the error due to the radiative transfer model. The total uncertainty of the proposed method in all bands is less than 5.16%. [ABSTRACT FROM AUTHOR]

Real-time monitoring of riverine-dissolved organic carbon (DOC) and its controlling factors is critical for formulating strategies regarding the river basin and marginal seas pollution prevention and control. In this study, we established a linear regression formulation that relates the permanganate index (CODMn) to the DOC concentration based on in situ measurements collected on five field surveys in 2023–2024. This regression formulation was used on a large number of data collected from automatic monitoring stations in the Qiantang River area to construct a daily quasi-in situ database of DOC concentration. By combining the quasi-in situ DOC data and Sentinel-2 measurements, an enhanced algorithm for empirical DOC estimation was developed (R2 = 0.66) using the extreme gradient boosting (XGBoost) method and its spatial and temporal variations in the Qiantang River were analyzed from 2016 to 2023. Spatially, the main stream of the Qiantang River exhibited an overall decreasing and increasing trend influenced by population density, economic development, and pollutant discharge in the basin area, and the temporal distribution of DOC was controlled by meteorological conditions. The DOC contents had the highest in summer, primarily due to high rainfall and leaching. The inter-annual variation in DOC concentration was influenced by the total annual runoff volumes, with a minimum level of 2.24 mg L−1 in 2023 and a maximum level of 2.45 mg L−1 in 2019. The monthly DOC fluxes ranged from 6.3 to 13.8 × 104 t, with the highest values coinciding with the maximum river discharge volumes in June and July. The DOC levels in the Qiantang River remained relatively high in recent years (2016–2023). This study enables the concerned stakeholders and researchers to better understand carbon transportation and its dynamics in the Qiantang River and its coastal areas. [ABSTRACT FROM AUTHOR]

This paper presents the results of a three-year survey (2021–2023), conducted in an area of approximately 356 km2 in Iraqi Kurdistan with the aim of identifying previously undetected archaeological sites. Thanks to the development of a multi-temporal approach based on open multispectral satellite data, greater effectiveness was achieved for the recognition of archaeological sites when compared to the use of single archival or freely accessible satellite images, which are typically employed in archaeological research. In particular, the Google Earth Engine services allowed for the efficient utilization of cloud computing resources to handle hundreds of remote sensing images. Using different datasets, namely Landsat 5, Landsat 7 and Sentinel-2, several products were obtained by processing entire stacks of images acquired at different epochs, thus minimizing the adverse effects on site visibility caused by vegetation, crops and cloud coverage and permitting an effective visual inspection and site recognition. Furthermore, spectral signature analysis of every potential site complemented the method. The developed approach was tested on areas that belong to the Land of Nineveh Archaeological Project (LoNAP) and the Upper Greater Zab Archaeological Reconnaissance (UGZAR) project, which had been intensively surveyed in the recent past. This represented an additional challenge to the method, as the most visible and extensive sites (tells) had already been detected. Three years of direct ground-truthing in the field enabled assessment of the outcomes of the remote sensing-based analysis, discovering more than 60 previously undetected sites and confirming the utility of the method for archaeological research in the area of Northern Mesopotamia. [ABSTRACT FROM AUTHOR]

The European spruce bark beetle is a major disturbance agent in Norway spruce forests in Europe, and with a changing climate it is predicted that damage will increase. To prevent the bark beetle population buildup, and to limit further spread during outbreaks, it is crucial to detect attacked trees early. In this study, we utilize Sentinel-2 data in combination with a risk map, created from geodata and forestry data, to detect trees predisposed to and attacked by the European spruce bark beetle. Random forest models were trained over two tiles (90 × 90 km) in southern Sweden for all dates with a sufficient number of cloud-free Sentinel-2 pixels during the period May–September in 2017 and 2018. The pixels were classified into attacked and healthy to study how detection accuracy changed with time after bark beetle swarming and to find which Sentinel-2 bands are more important for detecting bark beetle attacked trees. Random forest models were trained with (1) single-date data, (2) temporal features (1-year difference), (3) single-date and temporal features combined, and (4) Sentinel-2 data and a risk map combined. We also included a spatial variability metric. The results show that detection accuracy was high already before the trees were attacked in May 2018, indicating that the Sentinel-2 data detect predisposed trees and that the early signs of attack are low for trees at high risk of being attacked. For single-date models, the accuracy ranged from 63 to 79% and 84 to 94% for the two tiles. For temporal features, accuracy ranged from 65 to 81% and 81 to 92%. When the single-date and temporal features were combined, the accuracy ranged from 70 to 84% and 90 to 96% for the two tiles, and with the risk map included, the accuracy ranged from 83 to 91% and 92 to 97%, showing that remote sensing data and geodata can be combined to increase detection accuracy. The differences in accuracy between the two tiles indicate that local differences can influence accuracy, suggesting that geographically weighted methods should be applied. For the single-date models, the SWIR, red-edge, and blue bands were generally more important, and the SWIR bands were more important after the attack, suggesting that they are most suitable for detecting the early signs of a bark beetle attack. For the temporal features, the SWIR and blue bands were more important, and for the variability metric, the green band was generally more important. [ABSTRACT FROM AUTHOR]

Water content as an important physiological status variable in plants, is closely linked to transpiration, photosynthesis, water stress, and biomass productivity. Obtaining plant water information that provides sufficient spatial and temporal resolution for such applications remains a challenge. In this study, the data distribution space of fractional vegetation cover (FVC) and shortwave infrared transformed reflectance (STR) with linear and nonlinear edges was used to monitor plant water content. Four indicators of plant water status, equivalent water thickness (EWT), leaf water content (LWC), relative water content (RWC), and leaf water potential (LWP), were measured in a 10-ha corn field at the Karkaj Agricultural Research Station of Tabriz University, Tabriz, Iran. Furthermore, the Sentinel-2 Level 2 prototype processor (SL2P) was utilized to estimate EWT and compare the results with FVC-STR space. The FVC-STR space with nonlinear edges (FSNLE) provided better estimation accuracy of plant water status than the FVC-STR space with linear edges (FSLE). The root mean square errors of the EWT, LWC, RWC and LWP estimates for FSLE were 0.00306 g/cm2, 4.03 %, 6.56 %, and 0.38 bar, respectively, while those for FSNLE were 0.00303 g/cm2, 3.75 %, 5.57 %, and 0.37 bar, respectively. In addition, the R2 value for FSNLE was higher than that for FSLE (0.43–0.70 vs. 0.39–0.64). The RMSE and R2 for SL2P were 0.0041 g/cm2 and 0.401, respectively. Among the four measured indicators, the highest and lowest estimation accuracies in both FSLE and FSNLE were obtained with EWT and RWC, respectively. It can be concluded that FVC-STR space model based on Sentinel-2 imagery data provide acceptable accuracy for estimating plant water content. The FVC-STR space with nonlinear edges provided a better estimation accuracy for plant water indicators than the FVC-STR space with linear edges. [ABSTRACT FROM AUTHOR]

Introduction: The Common Agricultural Policy (CAP) is a vital policy framework implemented by the European Union to regulate and support agricultural production within member states. The Land Parcel Identification System (LPIS) is a key component that provides reliable land identification for administrative control procedures. On-the-spot checks (OTSC) are carried out to verify compliance with CAP requirements, typically relying on visual interpretation or field visits. However, the CAP is embracing advanced technologies to enhance its efficiency. Methods: This study focuses on using Sentinel-2 time series data and a two-level approach involving recurrent neural networks (RNN) and convolutional neural networks (CNN) to accurately identify permanent pastures. Results: In the first step, using RNN, the model achieved an accuracy of 68%, a precision of 36%, a recall of 97% and a F1-score of 52%, which indicates the model's ability to identify all the true positive parcels (correctly identified permanent pasture parcels) and minimize the false negative parcels (non-identified permanent pasture parcels). This occurs due to the difficulty in distinguishing between permanent pastures and other similar land covers (such as temporary pastures and shrublands). In the second step, it was possible to distinguish the permanent pasture parcels from the others. The obtained results improved significantly from the first to the second step. Using CNN, an accuracy of 93%, a precision of 89%, and a recall of 98% were achieved for the "Permanent pasture" class. The F1-score was 94%, indicating a balanced measure of the model's performance. Discussion: The integration of advanced technologies in the CAP's control mechanisms, as demonstrated, has the potential to automate the verification of farmers' declarations and subsequent subsidy payments. [ABSTRACT FROM AUTHOR]